Meme Sentiment

Meme sentiment analysis focuses on automatically determining the emotional tone conveyed by memes, which are multimodal data combining images and text. Current research emphasizes developing robust multimodal models, often employing deep learning architectures like those based on CLIP or incorporating techniques such as hierarchical embeddings and contrastive learning, to effectively capture the interplay between visual and textual components. This field is significant for understanding online communication, detecting harmful content like propaganda and hate speech, and improving the accuracy of sentiment analysis in diverse digital contexts. The development of open-source toolkits like MATK is also facilitating progress and reproducibility within the research community.